1. Data Analysis By Dr Zamhar Iswandono Ismail SSIL

2. Data Generation and Collection Methods Interviews: conversations between people which have assumptions and purpose for extracting data from respondents. Observations: seeing, hearing, noting, analysing, forming theories, making inferences, imposing meaning. Questionnaires: a pre-defined set of questions, assembled in a pre-determined order which are then answered by respondents providing data that will be analysed and interpreted. Documents: there are two types, found and research- generated documents.

3. Quantitative Data Analysis Quantitative data is data or evidence based on numbers. The idea of data analysis is to look for patterns in the data and draw conclusions. For small-scale projects, you only need simple quantitative analysis techniques. For large projects, you need more complex techniques. There are many computer-based programs for quantitative data analysis, which makes life easier for many researchers, e.g. SPSS.

4. Quantitative Data Analysis Data Coding Some of your data may already be in numeric form, other data needs to be coded in a number if you want to enter data easily for quantitative data analysis. If you used a questionnaire you need to code each predefined answer option you included; and code each theme which appears in respondents' answers to open questions. One tip is to create a code book. Note each code used and the kind of data it applies to.

5. Quantitative Data Analysis Visual aids for quantitative data analysis: They assist in visualising the results of data collection for analysis and presentation. Tables (Jadual) Charts (Carta)  Bar charts  Pie charts Graphs (Geraf)  Scatter graphs  Line graphs

6. Using Statistics for Quantitative Data Analysis Describing the central tendency Mean Median Mode Describing the distribution Range Fractiles Standard deviation Finding relationships in the data Correlation coefficients The null hypothesis and tests of significance Chi-square test T-tests

7. Interpretation of Quantitative Data Analysis Results After analysing the data, you still need to interpret the results for your research. Think about: What do your results show? What do they imply? How do they relate to other reported research in the literature about your research topic? Do your findings agree or disagree with those of other researchers or people in authority? What do you think is important in your results? What relevance do they have for other researchers? What relevance do they have for people in the real world beyond universities?

8. Evaluating Quantitative Data Analysis Advantages: Provides (seeming) scientific respectability Analysis is based on well-established techniques Analysis is based on measured techniques. Large volumes can be analysed quickly. Disadvantages: Many people dislike working with numbers. Danger of doing more statistical tests. Analysis is only as good as initial generated data. Have to be clear abot statistical test Not as scientifically objective as it appears.

9. Qualitative Data Analysis Qualitative data includes all non-numeric data – words, images, sounds, and so on. It is the main type of data or evidence generated by case studies, action research and ethnography. You can use quantitative analysis on qualitative data like by counting the number of times a word/phrase turns up in interviews. However, most qualitative data analysis involves abstracting from the research data the verbal, visual or aural themes and patterns that you think are important to your research topic. Qualitative researchers have been criticised for not providing enough information about data analysis, there is a need to reflect upon data analysis so the researcher can describe the analysis process to others. Qualitative analysis is not as straightforward as quantitative.

10. Analysing Textual Data Data Preparation Prepare data to make it ready for analysis. Prepare transcripts of the data. Normally, researchers will use left margin for codes and right margin for notes. Plan an efficient filing system. Make duplicates and backups. Data Analysis Start by reading through all data Categorise the text from existing theories and observed data Refine the categories as the research goes on Consistently read, categorise and review Look for themes and inter-connections beteen segments and categories. Use visual aids like tables and diagrams Keep notes about the analysis process

11. Analysing Non-textual Qualitative Data Includes audio tapes or sound clips, videos, photos and multimedia documents Data preparation is similar to textual data Some researcher treat images as true representations of the environment, as an ethnographic account. As with textual data, you need to look for themes and patterns. An image can be considered a cultural artefact. If analysing video or animation, look out for: Denotation: what is being depicted ub the piece, what style or genre? Connotation: what ideas or values are being presented? Author: who produced it, circumstances and why? Viewer: how does a viewer interpret or react to it?

12. Grounded Theory Proposed by Glaser and Strauss (1967) as an inductive approach to theory generation. A particular approach to qualitative research where the intention is to do field research and analyse data to see what theory emerges. The theory is grounded in the field data. Concerned with generating theories. The data analysis has three phases: Open coding Axial coding Selective coding

13. Evaluating Qualitative Data Analysis Advantages: Data and analysis can be rich and detailed. Possibility of alternative explanations. Different researchers might reach different conclusions. Disadvantages: Danger of being overwhelmed by volume of qualitative data. Interpretation of data is tied to researcher. Non-textual data does not fit easily into theses and papers.

14. Personal Thoughts If you ever wondered, what's the difference between a Degree holder and someone who knows programming/multimedia/web-authoring without a degree. This is the difference. Your FYP is the convergence of theory, methodology and application. If you enjoy doing this, please consider a career in academia.

15. May Allah Bless You and Make Things Easy During Your Final Year Project. Aameen. CHEERS!